The purpose of our research project is to develop a distribution network design model taking into account many realistic features arising from a case-study in the field of car distribution. The overall network structure consists of three levels: plants, distribution centres (DCs) and customers. We assume that the number and location of the plants as well as the number and location of the customers are fixed. Given the demand of customers and a list of potential DCs, our main concern is to locate DCs and to assign customers to them in such a way as to minimize the total distribution costs. In terms of problem modeling, we integrate various operational features that were considered separately in the literature but have never been combined in a same model. Namely, we introduce a clustering-based approach to model vehicle routing, minimum volume constraints to ensure full truckload transport, minimum and maximum throughput constraints on DCs, maximum covering distance constraints and single sourcing restrictions. Furthermore, we study a multi-period extension of the problem using an original dynamic clustering to model multi-period vehicle routing. In terms of solution method, as the problem we study is NP-hard in the strong sense, we propose efficient heuristic procedures based on various types of linear relaxation. Through our numerical experiments, we show that the implemented heuristics offer near-optimal solutions with less computational effort than applying an exact MIP solver. We also analyze the structure of the obtained networks and compare the results of several versions of the model, highlighting the value of integrating a pre-processing clustering step and of using a multi-period approach.
